Abstract

Decades of AI research have yielded techniques for learning,
inference, and planning that depend on human-provided ontologies of
self, space, time, objects, actions, and properties. Since robots
are constructed with low-level sensor and motor interfaces that do
not provide these concepts, the human robotics researcher must
create the bindings between the required high-level concepts and the
available low-level interfaces. This raises the developmental
learning problem for robots of how a learning agent can create
high-level concepts from its own low-level experience.
Prior work has shown how objects can be individuated from low-level
sensation, and certain properties can be learned for individual
objects. This work shows how high-level actions can be learned
autonomously by searching for control laws that reliably change
these properties in predictable ways. We present a robust and
efficient algorithm that creates reliable control laws for perceived
objects. We demonstrate on a physical robot how these high-level
actions can be learned from the robot's own experiences, and can
then applied to a learned object to achieve a desired goal.